Jointly Heterogeneous Palmprint Discriminant Feature Learning

Heterogeneous palmprint recognition has attracted considerable research attention in recent years because it has the potential to greatly improve the recognition performance for personal authentication. In this article, we propose a simultaneous heterogeneous palmprint feature learning and encoding method for heterogeneous palmprint recognition. Unlike existing hand-crafted palmprint descriptors that usually extract features from raw pixels and require strong prior knowledge to design them, the proposed method automatically learns the discriminant binary codes from the informative direction convolution difference vectors of palmprint images. Differing from most heterogeneous palmprint descriptors that individually extract palmprint features from each modality, our method jointly learns the discriminant features from heterogeneous palmprint images so that the specific discriminant properties of different modalities can be better exploited. Furthermore, we present a general heterogeneous palmprint discriminative feature learning model to make the proposed method suitable for multiple heterogeneous palmprint recognition. Experimental results on the widely used PolyU multispectral palmprint database clearly demonstrate the effectiveness of the proposed method.